From 02368917cd906cb57eac3c3f0df2055c68da0b3b Mon Sep 17 00:00:00 2001
From: aakan96 <aakan96@mi.fu-berlin.de>
Date: Tue, 11 Jul 2023 18:55:00 +0000
Subject: [PATCH] Neue Datei hochladen
---
.../DS_mRNA_limma_dataset_svm_F.ipynb | 1866 +++++++++++++++++
1 file changed, 1866 insertions(+)
create mode 100644 Machine Learning/DS_mRNA_limma_dataset_svm_F.ipynb
diff --git a/Machine Learning/DS_mRNA_limma_dataset_svm_F.ipynb b/Machine Learning/DS_mRNA_limma_dataset_svm_F.ipynb
new file mode 100644
index 0000000..3428ccc
--- /dev/null
+++ b/Machine Learning/DS_mRNA_limma_dataset_svm_F.ipynb
@@ -0,0 +1,1866 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 113,
+ "id": "f097ad55",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')\n",
+ "import pandas as pd\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "#from sklearn.model_selection import cross_val_score\n",
+ "#from sklearn.metrics import accuracy_score\n",
+ "#import sklearn.metrics as metrics\n",
+ "#from sklearn.metrics import auc\n",
+ "from sklearn.metrics import RocCurveDisplay\n",
+ "#from sklearn.model_selection import KFold\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "from imblearn.over_sampling import SMOTE\n",
+ "from sklearn.linear_model import Lasso\n",
+ "import xgboost as xgb\n",
+ "from sklearn.model_selection import GridSearchCV\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "np.random.seed(7)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "73b6611a",
+ "metadata": {},
+ "source": [
+ "# Data Preprocessing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 114,
+ "id": "0eeb7a35",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train = pd.read_csv(\"DS/mRNA_DS_preprocessed_training_data.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 126,
+ "id": "a6ab23aa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test = pd.read_csv(\"DS/mRNA_DS_test_data.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 127,
+ "id": "683b63ce",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train = df_train.T\n",
+ "df_test = df_test.T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 128,
+ "id": "7928107a",
+ "metadata": {},
+ "outputs": [
+ {
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>Gene symbol</th>\n",
+ " <td>MIR4640///DDR1</td>\n",
+ " <td>RFC2</td>\n",
+ " <td>PAX8</td>\n",
+ " <td>MIR5193///UBA7</td>\n",
+ " <td>CYP2E1</td>\n",
+ " <td>MMP14</td>\n",
+ " <td>PLD1</td>\n",
+ " <td>DTX2P1-UPK3BP1-PMS2P11</td>\n",
+ " <td>CAPNS1</td>\n",
+ " <td>HNRNPC</td>\n",
+ " <td>...</td>\n",
+ " <td>C11orf24</td>\n",
+ " <td>B4GALT7</td>\n",
+ " <td>DVL2</td>\n",
+ " <td>RBKS</td>\n",
+ " <td>SENP5</td>\n",
+ " <td>POLR2J4</td>\n",
+ " <td>INO80B-WBP1///INO80B</td>\n",
+ " <td>SNHG17</td>\n",
+ " <td>MEX3D</td>\n",
+ " <td>DCAF15</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>61 rows × 4857 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " 0 1 2 3 4 \\\n",
+ "GSM935144 8.369057 6.512733 7.566895 8.583529 3.89921 \n",
+ "GSM935145 10.480503 7.291269 7.39136 6.463924 5.363036 \n",
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+ "GSM935148 8.34986 6.783343 7.110082 7.492759 3.77593 \n",
+ "... ... ... ... ... ... \n",
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+ "GSM935203 10.68054 7.447833 8.118057 6.53307 3.952315 \n",
+ "Gene symbol MIR4640///DDR1 RFC2 PAX8 MIR5193///UBA7 CYP2E1 \n",
+ "\n",
+ " 5 6 7 8 9 \\\n",
+ "GSM935144 7.500077 5.286338 8.024788 10.163873 10.411274 \n",
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+ "GSM935148 7.289664 5.335561 7.943047 10.453795 10.084535 \n",
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+ "GSM935203 7.598421 6.257321 8.258537 10.437635 10.576629 \n",
+ "Gene symbol MMP14 PLD1 DTX2P1-UPK3BP1-PMS2P11 CAPNS1 HNRNPC \n",
+ "\n",
+ " ... 4847 4848 4849 4850 4851 4852 \\\n",
+ "GSM935144 ... 8.397834 6.495765 7.682125 5.636567 4.662533 4.581447 \n",
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+ "GSM935148 ... 8.699464 6.098069 7.788782 5.897471 4.804696 4.15752 \n",
+ "... ... ... ... ... ... ... ... \n",
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+ "GSM935203 ... 8.648412 6.04367 7.982976 5.885975 6.998636 4.572075 \n",
+ "Gene symbol ... C11orf24 B4GALT7 DVL2 RBKS SENP5 POLR2J4 \n",
+ "\n",
+ " 4853 4854 4855 4856 \n",
+ "GSM935144 4.913772 7.314994 4.946271 5.824582 \n",
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+ "GSM935147 6.16125 7.9986 8.799876 6.02798 \n",
+ "GSM935148 4.363255 7.179146 6.736683 5.498095 \n",
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+ "GSM935202 4.801002 7.021587 5.007519 6.399793 \n",
+ "GSM935203 5.612742 7.441464 4.96548 6.258775 \n",
+ "Gene symbol INO80B-WBP1///INO80B SNHG17 MEX3D DCAF15 \n",
+ "\n",
+ "[61 rows x 4857 columns]"
+ ]
+ },
+ "execution_count": 128,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 129,
+ "id": "a701e30d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#df_test = df_test[:-1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 130,
+ "id": "77d974be",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Transform the input data\n",
+ "df_train.rename(columns=df_train.iloc[0], inplace = True)\n",
+ "df_train.drop(df_train.index[0], inplace = True)\n",
+ "df_train=df_train.reset_index()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 131,
+ "id": "2e78017d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Transform the input data\n",
+ "df_test.rename(columns=df_test.iloc[-1], inplace = True)\n",
+ "df_test.drop(df_test.index[-1], inplace = True)\n",
+ "df_test=df_test.reset_index()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 132,
+ "id": "ea60801d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "metadata_test = pd.read_csv(\"DS/mRNA_DS_metadata_col_test_info.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 133,
+ "id": "58d531b9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test= df_test.merge(metadata_test, left_on=\"index\", right_on= \"Unnamed: 0\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 134,
+ "id": "7910f2fa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test['title0'] = df_test['title0'].replace('(?i)mucosa|normal|healthy', 0, regex=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 135,
+ "id": "c102e10e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test['title0'] = df_test['title0'].replace('(?i)Tumor|Cancer|carcinoma', 1, regex=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 136,
+ "id": "6c255d2e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "title0\n",
+ "0 30\n",
+ "1 30\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 136,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_test['title0'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 125,
+ "id": "636b44ab",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test = df_test[pd.to_numeric(df_test['title0'], errors='coerce').notnull()]#remove all non-numeric data from the column."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "id": "34896f9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test= df_test.drop(['index', 'Unnamed: 0'], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "id": "359b5bab",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_test= df_test.rename(columns={\"title0\": \"index\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "id": "dc14bb1c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_test=df_test.drop(\"index\",axis=1)\n",
+ "y_test=df_test['index']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "id": "4c50c510",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "metadata_train = pd.read_csv(\"DS/mRNA_DS_metadata_col_info.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "id": "6730cf89",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train= df_train.merge(metadata_train, left_on=\"index\", right_on= \"Unnamed: 0\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "id": "7a8ad8ad",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train['title0'] = df_train['title0'].replace('(?i)mucosa|normal|healthy', 0, regex=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "id": "a8cf8643",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train['title0'] = df_train['title0'].replace('(?i)Tumor|Cancer|carcinoma', 1, regex=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "c9e8772b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "title0\n",
+ "0 111\n",
+ "1 108\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 71,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_train['title0'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "id": "f5d203aa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train = df_train[pd.to_numeric(df_train['title0'], errors='coerce').notnull()]#remove all non-numeric data from the column."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "id": "523bdaa6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train= df_train.drop(['index', 'Unnamed: 0'], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "id": "46a6fb36",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train= df_train.rename(columns={\"title0\": \"index\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "id": "e26f88c5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "index\n",
+ "0 111\n",
+ "1 108\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 75,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_train['index'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "fbaf2507",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_train= df_train.apply(pd.to_numeric)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "id": "38a993d9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sklearn.manifold import TSNE\n",
+ "# Assuming your data is stored in the variable 'data'\n",
+ "tsne = TSNE(n_components=3)\n",
+ "embedded_data = tsne.fit_transform(df_train)\n",
+ "\n",
+ "# Step 2: Separate data points by class\n",
+ "class_1_indices = np.where(df_train['index'] == 0)[0]\n",
+ "class_2_indices = np.where(df_train['index'] == 1)[0]\n",
+ "\n",
+ "class_1_data = embedded_data[class_1_indices]\n",
+ "class_2_data = embedded_data[class_2_indices]\n",
+ "\n",
+ "# Step 3: Plot the t-SNE plot with different colors for each class\n",
+ "plt.scatter(class_1_data[:, 0], class_1_data[:, 1], color='red', label='Non-Cancer')\n",
+ "plt.scatter(class_2_data[:, 0], class_2_data[:, 1], color='blue', label='Cancer')\n",
+ "\n",
+ "plt.title('t-SNE Plot')\n",
+ "plt.xlabel('Dimension 1')\n",
+ "plt.ylabel('Dimension 2')\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "id": "776cfbee",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "index\n",
+ "0 111\n",
+ "1 108\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 78,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_train['index'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "id": "8c0011ea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X=df_train.drop(\"index\",axis=1)\n",
+ "y=df_train['index']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "id": "fc606979",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X=X.astype('int')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "id": "93e28118",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y=y.astype('int')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e9830b6c",
+ "metadata": {},
+ "source": [
+ "# Feature Selection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "id": "f0f1977f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>ABAT</th>\n",
+ " <th>ABHD5</th>\n",
+ " <th>ABLIM1</th>\n",
+ " <th>ABLIM3</th>\n",
+ " <th>ACAA1</th>\n",
+ " <th>ACADM</th>\n",
+ " <th>ACADVL</th>\n",
+ " <th>ACD</th>\n",
+ " <th>ACLY</th>\n",
+ " <th>ACOT11</th>\n",
+ " <th>...</th>\n",
+ " <th>XYLT1</th>\n",
+ " <th>YOD1</th>\n",
+ " <th>YTHDC1</th>\n",
+ " <th>ZBTB16</th>\n",
+ " <th>ZDHHC13</th>\n",
+ " <th>ZFP64</th>\n",
+ " <th>ZNF185</th>\n",
+ " <th>ZNF365</th>\n",
+ " <th>ZNF426</th>\n",
+ " <th>ZNF710</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>186</td>\n",
+ " <td>2603</td>\n",
+ " <td>42653</td>\n",
+ " <td>220</td>\n",
+ " <td>2132</td>\n",
+ " <td>22869</td>\n",
+ " <td>19775</td>\n",
+ " <td>4486</td>\n",
+ " <td>8835</td>\n",
+ " <td>2332</td>\n",
+ " <td>...</td>\n",
+ " <td>392</td>\n",
+ " <td>222</td>\n",
+ " <td>295</td>\n",
+ " <td>4598</td>\n",
+ " <td>7009</td>\n",
+ " <td>568</td>\n",
+ " <td>65123</td>\n",
+ " <td>56</td>\n",
+ " <td>308</td>\n",
+ " <td>10385</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>93</td>\n",
+ " <td>1137</td>\n",
+ " <td>16493</td>\n",
+ " <td>69</td>\n",
+ " <td>1816</td>\n",
+ " <td>17788</td>\n",
+ " <td>16870</td>\n",
+ " <td>7993</td>\n",
+ " <td>21434</td>\n",
+ " <td>2211</td>\n",
+ " <td>...</td>\n",
+ " <td>62</td>\n",
+ " <td>78</td>\n",
+ " <td>144</td>\n",
+ " <td>2132</td>\n",
+ " <td>2602</td>\n",
+ " <td>1720</td>\n",
+ " <td>13531</td>\n",
+ " <td>47</td>\n",
+ " <td>140</td>\n",
+ " <td>6441</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>198</td>\n",
+ " <td>5593</td>\n",
+ " <td>53918</td>\n",
+ " <td>263</td>\n",
+ " <td>3490</td>\n",
+ " <td>39276</td>\n",
+ " <td>25847</td>\n",
+ " <td>4413</td>\n",
+ " <td>9212</td>\n",
+ " <td>7419</td>\n",
+ " <td>...</td>\n",
+ " <td>481</td>\n",
+ " <td>355</td>\n",
+ " <td>308</td>\n",
+ " <td>1071</td>\n",
+ " <td>10289</td>\n",
+ " <td>379</td>\n",
+ " <td>65131</td>\n",
+ " <td>206</td>\n",
+ " <td>1251</td>\n",
+ " <td>11768</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>104</td>\n",
+ " <td>1636</td>\n",
+ " <td>19203</td>\n",
+ " <td>127</td>\n",
+ " <td>1518</td>\n",
+ " <td>17951</td>\n",
+ " <td>16854</td>\n",
+ " <td>12800</td>\n",
+ " <td>11939</td>\n",
+ " <td>5136</td>\n",
+ " <td>...</td>\n",
+ " <td>213</td>\n",
+ " <td>122</td>\n",
+ " <td>244</td>\n",
+ " <td>482</td>\n",
+ " <td>3578</td>\n",
+ " <td>1990</td>\n",
+ " <td>37715</td>\n",
+ " <td>66</td>\n",
+ " <td>361</td>\n",
+ " <td>8517</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>205</td>\n",
+ " <td>4720</td>\n",
+ " <td>56984</td>\n",
+ " <td>495</td>\n",
+ " <td>3309</td>\n",
+ " <td>24427</td>\n",
+ " <td>28197</td>\n",
+ " <td>5718</td>\n",
+ " <td>8192</td>\n",
+ " <td>6748</td>\n",
+ " <td>...</td>\n",
+ " <td>169</td>\n",
+ " <td>275</td>\n",
+ " <td>200</td>\n",
+ " <td>3632</td>\n",
+ " <td>7275</td>\n",
+ " <td>509</td>\n",
+ " <td>65138</td>\n",
+ " <td>188</td>\n",
+ " <td>587</td>\n",
+ " <td>9390</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>214</th>\n",
+ " <td>4</td>\n",
+ " <td>5</td>\n",
+ " <td>9</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " <td>10</td>\n",
+ " <td>8</td>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " <td>...</td>\n",
+ " <td>7</td>\n",
+ " <td>4</td>\n",
+ " <td>9</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " <td>6</td>\n",
+ " <td>5</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>215</th>\n",
+ " <td>5</td>\n",
+ " <td>5</td>\n",
+ " <td>8</td>\n",
+ " <td>8</td>\n",
+ " <td>8</td>\n",
+ " <td>8</td>\n",
+ " <td>9</td>\n",
+ " <td>8</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>...</td>\n",
+ " <td>7</td>\n",
+ " <td>4</td>\n",
+ " <td>9</td>\n",
+ " <td>6</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>9</td>\n",
+ " <td>6</td>\n",
+ " <td>5</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>216</th>\n",
+ " <td>4</td>\n",
+ " <td>5</td>\n",
+ " <td>9</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>8</td>\n",
+ " <td>9</td>\n",
+ " <td>8</td>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " <td>...</td>\n",
+ " <td>7</td>\n",
+ " <td>5</td>\n",
+ " <td>9</td>\n",
+ " <td>6</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>8</td>\n",
+ " <td>6</td>\n",
+ " <td>5</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>217</th>\n",
+ " <td>5</td>\n",
+ " <td>4</td>\n",
+ " <td>9</td>\n",
+ " <td>7</td>\n",
+ " <td>8</td>\n",
+ " <td>8</td>\n",
+ " <td>9</td>\n",
+ " <td>8</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>...</td>\n",
+ " <td>7</td>\n",
+ " <td>4</td>\n",
+ " <td>10</td>\n",
+ " <td>6</td>\n",
+ " <td>8</td>\n",
+ " <td>7</td>\n",
+ " <td>8</td>\n",
+ " <td>6</td>\n",
+ " <td>5</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>218</th>\n",
+ " <td>4</td>\n",
+ " <td>4</td>\n",
+ " <td>7</td>\n",
+ " <td>6</td>\n",
+ " <td>8</td>\n",
+ " <td>8</td>\n",
+ " <td>10</td>\n",
+ " <td>8</td>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " <td>...</td>\n",
+ " <td>6</td>\n",
+ " <td>5</td>\n",
+ " <td>10</td>\n",
+ " <td>6</td>\n",
+ " <td>6</td>\n",
+ " <td>7</td>\n",
+ " <td>6</td>\n",
+ " <td>5</td>\n",
+ " <td>5</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>219 rows × 578 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ABAT ABHD5 ABLIM1 ABLIM3 ACAA1 ACADM ACADVL ACD ACLY ACOT11 \\\n",
+ "0 186 2603 42653 220 2132 22869 19775 4486 8835 2332 \n",
+ "1 93 1137 16493 69 1816 17788 16870 7993 21434 2211 \n",
+ "2 198 5593 53918 263 3490 39276 25847 4413 9212 7419 \n",
+ "3 104 1636 19203 127 1518 17951 16854 12800 11939 5136 \n",
+ "4 205 4720 56984 495 3309 24427 28197 5718 8192 6748 \n",
+ ".. ... ... ... ... ... ... ... ... ... ... \n",
+ "214 4 5 9 6 7 7 10 8 7 7 \n",
+ "215 5 5 8 8 8 8 9 8 6 7 \n",
+ "216 4 5 9 6 7 8 9 8 7 7 \n",
+ "217 5 4 9 7 8 8 9 8 6 7 \n",
+ "218 4 4 7 6 8 8 10 8 7 7 \n",
+ "\n",
+ " ... XYLT1 YOD1 YTHDC1 ZBTB16 ZDHHC13 ZFP64 ZNF185 ZNF365 ZNF426 \\\n",
+ "0 ... 392 222 295 4598 7009 568 65123 56 308 \n",
+ "1 ... 62 78 144 2132 2602 1720 13531 47 140 \n",
+ "2 ... 481 355 308 1071 10289 379 65131 206 1251 \n",
+ "3 ... 213 122 244 482 3578 1990 37715 66 361 \n",
+ "4 ... 169 275 200 3632 7275 509 65138 188 587 \n",
+ ".. ... ... ... ... ... ... ... ... ... ... \n",
+ "214 ... 7 4 9 6 7 7 7 6 5 \n",
+ "215 ... 7 4 9 6 6 7 9 6 5 \n",
+ "216 ... 7 5 9 6 6 7 8 6 5 \n",
+ "217 ... 7 4 10 6 8 7 8 6 5 \n",
+ "218 ... 6 5 10 6 6 7 6 5 5 \n",
+ "\n",
+ " ZNF710 \n",
+ "0 10385 \n",
+ "1 6441 \n",
+ "2 11768 \n",
+ "3 8517 \n",
+ "4 9390 \n",
+ ".. ... \n",
+ "214 8 \n",
+ "215 8 \n",
+ "216 8 \n",
+ "217 8 \n",
+ "218 8 \n",
+ "\n",
+ "[219 rows x 578 columns]"
+ ]
+ },
+ "execution_count": 82,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "id": "1cc528fb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# LASSO model:\n",
+ "lasso = Lasso(alpha=1)\n",
+ "# fitting the model:\n",
+ "lasso.fit(X, y)\n",
+ "# select all coefficients and the feature names\n",
+ "lasso_coefs = lasso.coef_\n",
+ "feature_names = X.columns\n",
+ "\n",
+ "# collect the selected features:\n",
+ "selected_feature_indices = np.nonzero(lasso_coefs)[0]\n",
+ "selected_features = [feature_names[i] for i in selected_feature_indices]\n",
+ "X_selected = X.iloc[:, selected_feature_indices]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "id": "8afa29ae",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "98"
+ ]
+ },
+ "execution_count": 84,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(selected_features)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6cee6462",
+ "metadata": {},
+ "source": [
+ "# Test train split"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "id": "3af09ef8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_train = X_selected\n",
+ "y_train = y"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "id": "129430e6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(index\n",
+ " 0 30\n",
+ " 1 30\n",
+ " Name: count, dtype: int64,\n",
+ " index\n",
+ " 0 111\n",
+ " 1 108\n",
+ " Name: count, dtype: int64)"
+ ]
+ },
+ "execution_count": 86,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_test.value_counts(),y_train.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1cfe2a06",
+ "metadata": {},
+ "source": [
+ "# Cross validation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "id": "1fbca4b8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.svm import SVC\n",
+ "# we can add class_weight='balanced' to add panalize mistake\n",
+ "svm_model = SVC(kernel = \"linear\", probability=True,random_state=47)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 102,
+ "id": "0502e118",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "# Defining parameter range\n",
+ "param_grid = {\n",
+ " 'C': [0.0005,0.0001,0.001,0.1]\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "id": "7f2d18b0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "grid = GridSearchCV(svm_model, param_grid, refit=True, verbose=3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "id": "79790f1d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n",
+ "[CV 1/5] END ..........................C=0.0005;, score=0.955 total time= 0.0s\n",
+ "[CV 2/5] END ..........................C=0.0005;, score=0.886 total time= 0.0s\n",
+ "[CV 3/5] END ..........................C=0.0005;, score=0.955 total time= 0.0s\n",
+ "[CV 4/5] END ..........................C=0.0005;, score=0.909 total time= 0.0s\n",
+ "[CV 5/5] END ..........................C=0.0005;, score=0.884 total time= 0.0s\n",
+ "[CV 1/5] END ..........................C=0.0001;, score=0.909 total time= 0.0s\n",
+ "[CV 2/5] END ..........................C=0.0001;, score=0.773 total time= 0.0s\n",
+ "[CV 3/5] END ..........................C=0.0001;, score=0.955 total time= 0.0s\n",
+ "[CV 4/5] END ..........................C=0.0001;, score=0.909 total time= 0.0s\n",
+ "[CV 5/5] END ..........................C=0.0001;, score=0.860 total time= 0.0s\n",
+ "[CV 1/5] END ...........................C=0.001;, score=0.955 total time= 0.0s\n",
+ "[CV 2/5] END ...........................C=0.001;, score=0.977 total time= 0.0s\n",
+ "[CV 3/5] END ...........................C=0.001;, score=0.955 total time= 0.0s\n",
+ "[CV 4/5] END ...........................C=0.001;, score=0.909 total time= 0.0s\n",
+ "[CV 5/5] END ...........................C=0.001;, score=0.884 total time= 0.0s\n",
+ "[CV 1/5] END .............................C=0.1;, score=0.864 total time= 0.0s\n",
+ "[CV 2/5] END .............................C=0.1;, score=0.955 total time= 0.0s\n",
+ "[CV 3/5] END .............................C=0.1;, score=0.977 total time= 0.0s\n",
+ "[CV 4/5] END .............................C=0.1;, score=0.977 total time= 0.0s\n",
+ "[CV 5/5] END .............................C=0.1;, score=0.953 total time= 0.0s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=SVC(kernel='linear', probability=True, random_state=47),\n",
+ " param_grid={'C': [0.0005, 0.0001, 0.001, 0.1]}, verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=SVC(kernel='linear', probability=True, random_state=47),\n",
+ " param_grid={'C': [0.0005, 0.0001, 0.001, 0.1]}, verbose=3)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel='linear', probability=True, random_state=47)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel='linear', probability=True, random_state=47)</pre></div></div></div></div></div></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "GridSearchCV(estimator=SVC(kernel='linear', probability=True, random_state=47),\n",
+ " param_grid={'C': [0.0005, 0.0001, 0.001, 0.1]}, verbose=3)"
+ ]
+ },
+ "execution_count": 104,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Fitting the model for grid search\n",
+ "grid.fit(X_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "id": "5d327876",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'C': 0.1}\n",
+ "SVC(C=0.1, kernel='linear', probability=True, random_state=47)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# print best parameter after tuning\n",
+ "print(grid.best_params_)\n",
+ " \n",
+ "# print how our model looks after hyper-parameter tuning\n",
+ "print(grid.best_estimator_)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 106,
+ "id": "f8d67e2f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Select columns in df1 based on columns in df2\n",
+ "X_test = X_test.loc[:, X_train.columns]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "id": "c8c233d6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.97 0.93 0.95 30\n",
+ " 1 0.94 0.97 0.95 30\n",
+ "\n",
+ " accuracy 0.95 60\n",
+ " macro avg 0.95 0.95 0.95 60\n",
+ "weighted avg 0.95 0.95 0.95 60\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import classification_report, confusion_matrix\n",
+ "grid_predictions = grid.predict(X_test)\n",
+ "print(classification_report(y_test, grid_predictions))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "id": "3b2776c0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"â–¸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"â–¾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=0.1, kernel='linear', probability=True, random_state=47)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.1, kernel='linear', probability=True, random_state=47)</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "SVC(C=0.1, kernel='linear', probability=True, random_state=47)"
+ ]
+ },
+ "execution_count": 108,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model_svm = grid.best_estimator_\n",
+ "model_svm.fit(X_train,y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "id": "94871ada",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_proba = model_svm.fit(X_train, y_train).predict_proba(X_test)[:,1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "id": "f8d4142d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import StratifiedKFold\n",
+ "from sklearn.feature_selection import SelectKBest, f_classif\n",
+ "from sklearn.metrics import auc\n",
+ "def roc(X_train,y_train,model,label):\n",
+ " cv = StratifiedKFold(n_splits=6)\n",
+ " classifier = model\n",
+ " tprs = []\n",
+ " aucs = []\n",
+ " mean_fpr = np.linspace(0, 1, 100)\n",
+ "\n",
+ " fig, ax = plt.subplots(figsize=(6, 6))\n",
+ " for fold, (train, test) in enumerate(cv.split(X_train, y_train)):\n",
+ " classifier.fit(X_train.iloc[train], y_train.iloc[train])\n",
+ " viz = RocCurveDisplay.from_estimator(\n",
+ " classifier,\n",
+ " X_train.iloc[test],\n",
+ " y_train.iloc[test],\n",
+ " name=f\"ROC fold {fold}\",\n",
+ " alpha=0.3,\n",
+ " lw=1,\n",
+ " ax=ax,\n",
+ " )\n",
+ " interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)\n",
+ " interp_tpr[0] = 0.0\n",
+ " tprs.append(interp_tpr)\n",
+ " aucs.append(viz.roc_auc)\n",
+ " ax.plot([0, 1], [0, 1], \"k--\", label=\"chance level (AUC = 0.5)\")\n",
+ "\n",
+ " mean_tpr = np.mean(tprs, axis=0)\n",
+ " mean_tpr[-1] = 1.0\n",
+ " mean_auc = auc(mean_fpr, mean_tpr)\n",
+ " std_auc = np.std(aucs)\n",
+ " ax.plot(\n",
+ " mean_fpr,\n",
+ " mean_tpr,\n",
+ " color=\"b\",\n",
+ " label=r\"Mean ROC (AUC = %0.2f $\\pm$ %0.2f)\" % (mean_auc, std_auc),\n",
+ " lw=2,\n",
+ " alpha=0.8,\n",
+ " )\n",
+ "\n",
+ " std_tpr = np.std(tprs, axis=0)\n",
+ " tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n",
+ " tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n",
+ " ax.fill_between(\n",
+ " mean_fpr,\n",
+ " tprs_lower,\n",
+ " tprs_upper,\n",
+ " color=\"grey\",\n",
+ " alpha=0.2,\n",
+ " label=r\"$\\pm$ 1 std. dev.\",\n",
+ " )\n",
+ "\n",
+ " ax.set(\n",
+ " xlim=[-0.05, 1.05],\n",
+ " ylim=[-0.05, 1.05],\n",
+ " xlabel=\"False Positive Rate\",\n",
+ " ylabel=\"True Positive Rate\",\n",
+ " title=label,\n",
+ " )\n",
+ " ax.axis(\"square\")\n",
+ " ax.legend(loc=\"lower right\")\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "id": "802a96a5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 600x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model = svm_model\n",
+ "label=\"ROC curve of training data\"\n",
+ "roc(X_train,y_train,model,label)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "id": "5b8b6681",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 600x600 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "label=\"ROC curve of testing data\"\n",
+ "roc(X_test,y_test,model,label)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "id": "033ca70b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#######CONFUSION MATRIX ###########\n",
+ "from sklearn import metrics\n",
+ "y_test_pred_svm = model_svm.predict(X_test)\n",
+ "confusion_matrix_test = metrics.confusion_matrix(y_test, y_test_pred_svm)\n",
+ "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix_test, display_labels = [False, True])\n",
+ "cm_display.plot()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 100,
+ "id": "2f9bc4a1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy : 0.95\n",
+ "Sensitivity : 0.9333333333333333\n",
+ "Specificity : 0.9666666666666667\n"
+ ]
+ }
+ ],
+ "source": [
+ "total1=sum(sum(confusion_matrix_test))\n",
+ "#####from confusion matrix calculate accuracy\n",
+ "accuracy1=(confusion_matrix_test[0,0]+confusion_matrix_test[1,1])/total1\n",
+ "print ('Accuracy : ', accuracy1)\n",
+ "\n",
+ "sensitivity1 = confusion_matrix_test[0,0]/(confusion_matrix_test[0,0]+confusion_matrix_test[0,1])\n",
+ "print('Sensitivity : ', sensitivity1 )\n",
+ "\n",
+ "specificity1 = confusion_matrix_test[1,1]/(confusion_matrix_test[1,0]+confusion_matrix_test[1,1])\n",
+ "print('Specificity : ', specificity1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 101,
+ "id": "8d6a7110",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#######CONFUSION MATRIX ###########\n",
+ "y_train_pred_svm = model_svm.predict(X_train)\n",
+ "confusion_matrix_train = metrics.confusion_matrix(y_train, y_train_pred_svm)\n",
+ "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix_train)\n",
+ "cm_display.plot()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 226,
+ "id": "81d0fac2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Accuracy : 0.9863013698630136\n",
+ "Sensitivity : 0.990990990990991\n",
+ "Specificity : 0.9814814814814815\n"
+ ]
+ }
+ ],
+ "source": [
+ "total1=sum(sum(confusion_matrix_train))\n",
+ "#####from confusion matrix calculate accuracy\n",
+ "accuracy1=(confusion_matrix_train[0,0]+confusion_matrix_train[1,1])/total1\n",
+ "print ('Accuracy : ', accuracy1)\n",
+ "\n",
+ "sensitivity1 = confusion_matrix_train[0,0]/(confusion_matrix_train[0,0]+confusion_matrix_train[0,1])\n",
+ "print('Sensitivity : ', sensitivity1 )\n",
+ "\n",
+ "specificity1 = confusion_matrix_train[1,1]/(confusion_matrix_train[1,0]+confusion_matrix_train[1,1])\n",
+ "print('Specificity : ', specificity1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 227,
+ "id": "c1095af0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# for important features:\n",
+ "important_feat = model_svm.coef_[0]\n",
+ "#get indices of those important features\n",
+ "idx = important_feat.argsort(kind= \"quicksort\")\n",
+ "idx= idx[::-1][:50]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 228,
+ "id": "ae7e0162",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df1 = X_selected.T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 229,
+ "id": "1d97f818",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "top_met = df1.iloc[idx]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 230,
+ "id": "4cd4227b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['COL1A1', 'ECT2', 'COL5A2', 'MCM2', 'MMP10', 'RPN1', 'TRIP13', 'FSCN1',\n",
+ " 'HSPBAP1', 'IGF2BP2', 'EFNA1', 'IGFBP3', 'EMP1', 'TMPRSS11E', 'PSMB9',\n",
+ " 'GABRP', 'NT5C2', 'RHCG', 'PITX1', 'RUVBL1', 'CYP4B1', 'SLC2A1',\n",
+ " 'LYPD3', 'GALNT1', 'IL1RN', 'TAPBP', 'DHRS2', 'SPRR3', 'SPINK5',\n",
+ " 'SCNN1A', 'TYMP', 'LAMC2', 'LEPROTL1', 'TSPAN6', 'INPP1', 'STK24',\n",
+ " 'SERPINB2', 'CRNN', 'MYH10', 'ECM1', 'HOPX', 'TFAP2B', 'IFI35',\n",
+ " 'TMPRSS11D', 'UCHL1', 'KRT4', 'AQP3', 'ACLY', 'ATP6V1D', 'TST'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 230,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "top_met.index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 232,
+ "id": "8f6d88bb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['ACLY', 'ACPP', 'AIM2', 'ALDH9A1', 'ALOX12', 'ANO1', 'AQP3', 'ATP6V1D',\n",
+ " 'CCNG2', 'CES2', 'CFD', 'CH25H', 'CLIC3', 'COL1A1', 'COL5A2', 'CRABP2',\n",
+ " 'CRISP3', 'CRNN', 'CYP4B1', 'DHRS1', 'DHRS2', 'DUOX1', 'DUSP5', 'ECM1',\n",
+ " 'ECT2', 'EFNA1', 'EMP1', 'ENTPD6', 'ERCC3', 'FLG', 'FSCN1', 'GABRP',\n",
+ " 'GALE', 'GALNT1', 'GPX3', 'HOPX', 'HSPB8', 'HSPBAP1', 'HSPD1', 'ID4',\n",
+ " 'IFI35', 'IGF2BP2', 'IGFBP3', 'IL1RN', 'INPP1', 'KANK1', 'KLK13',\n",
+ " 'KRT4', 'LAMC2', 'LCN2', 'LEPROTL1', 'LYPD3', 'MAL', 'MCM2', 'MMP10',\n",
+ " 'MUC1', 'MYH10', 'NDRG2', 'NT5C2', 'PCSK5', 'PHLDA1', 'PITX1',\n",
+ " 'PPP1R3C', 'PSMB9', 'PTN', 'RAB11FIP1', 'RANBP9', 'RHCG', 'RND3',\n",
+ " 'RPN1', 'RUVBL1', 'SCNN1A', 'SERPINB13', 'SERPINB2', 'SIM2', 'SLC2A1',\n",
+ " 'SLK', 'SLURP1', 'SPINK5', 'SPRR3', 'SSRP1', 'STK24', 'SYNPO2L',\n",
+ " 'TAPBP', 'TFAP2B', 'TGIF1', 'TIAM1', 'TJP1', 'TMF1', 'TMPRSS11D',\n",
+ " 'TMPRSS11E', 'TRIP13', 'TSPAN6', 'TST', 'TYMP', 'UCHL1', 'ZBTB16',\n",
+ " 'ZNF185'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 232,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_selected.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5d9ff727",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8b304c6c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c76098bb",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "321b6028",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--
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